Multi-agent reinforcement learning-based passenger spoofing attack on Mobility-as-a-Service

Show simple item record

dc.contributor.author Chu, Kai-Fung
dc.contributor.author Guo, Weisi
dc.date.accessioned 2024-04-02T14:05:32Z
dc.date.available 2024-04-02T14:05:32Z
dc.date.issued 2024-03-19
dc.identifier.citation Chu KF, Guo W. (2024) Multi-agent reinforcement learning-based passenger spoofing attack on Mobility-as-a-Service. IEEE Transactions on Dependable and Secure Computing. Available online 19 March 2024 en_UK
dc.identifier.issn 1545-5971
dc.identifier.uri https://doi.org/10.1109/TDSC.2024.3379283
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/21114
dc.description.abstract Cyber-physical systems, such as smart transportation, face security threats from both digital and physical realms. Recently, Mobility-as-a-Service (MaaS) has emerged as a novel transportation concept, offering passengers access to diverse mobility services via a unified platform. Central to this system is the smart MaaS coordinator, tasked with tailoring services to passengers based on their profiles and behaviors. However, the coordination of heterogeneous passengers introduces vulnerabilities, enabling malicious entities to exploit the system by impersonating priority passengers with falsified information. Effective detection mechanisms require a deep understanding of the spoofing process. This paper investigates threats to the smart MaaS coordinator, unveiling a new reinforcement learning-based attack named the passenger spoofing attack, which aims to mitigate the risk of inadvertently exposing MaaS vulnerabilities post-deployment. This attack leverages feedback from actions and experiences to manipulate system profitability and passenger satisfaction by generating false passenger information. Furthermore, our research reveals that multi-agent reinforcement learning, accounting for spatial distribution among malicious agents and passengers, strengthens the attack. Through simulations based on datasets from New York City and synthetic sources, we demonstrate that the attack can significantly reduce 70% of profit and 50% of passenger satisfaction. Spatial analysis indicates an effective distance of approximately two nodes from the origin or destination. This study enriches our comprehension of the vulnerabilities inherent in smart coordinators within MaaS, enabling the development of robust countermeasures against malicious actors. en_UK
dc.description.sponsorship This work was supported by EPSRC MACRO - Mobility as a service: Managing Cybersecurity Risks across Consumers, Organisations and Sectors (EP/V039164/1) en_UK
dc.language.iso en_UK en_UK
dc.publisher IEEE en_UK
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject Passenger spoofing attack en_UK
dc.subject multi-agent reinforcement learning en_UK
dc.subject multimodal transport en_UK
dc.subject intelligent transportation systems en_UK
dc.title Multi-agent reinforcement learning-based passenger spoofing attack on Mobility-as-a-Service en_UK
dc.type Article en_UK
dcterms.dateAccepted 2024-03-16


Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International Except where otherwise noted, this item's license is described as Attribution 4.0 International

Search CERES


Browse

My Account

Statistics